library(tidyverse)
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library(glue)
library(SingleCellExperiment)
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library(lemur)
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source("util.R")
fit <- qs::qread("../benchmark/output/cable_spatial_plaque_data/fit_small.qs")
nei <- qs::qread("../benchmark/output/cable_spatial_plaque_data/nei_small.qs")
mouse_labels <- structure(paste0("Mouse ", 1:4), names = paste0("mouse_", 1:4))
lemur.utils::count_labels_per_neighborhood(nei, labels = vars(class %in% c("Glutamatergic", "GABAergic")),
fit = fit, cell_names = seq_len(ncol(fit))) %>%
mutate(frac = counts / total_counts)
## # A tibble: 2 × 5
## name label counts total_counts frac
## <chr> <lgl> <dbl> <int> <dbl>
## 1 Jun TRUE 6183 30687 0.201
## 2 Jun FALSE 3 10368 0.000289
spatial_plaq_dens_pl <- as_tibble(colData(fit)) %>%
ggplot(aes(x = x, y = y)) +
ggrastr::rasterize(geom_point(aes(color = plaque_density), size = 0.05, stroke = 0), dpi = 600) +
scale_color_gradient(low = "lightgrey", high = "darkorange", limits = c(0, 1), breaks = c(0, 0.5, 1)) +
facet_wrap(vars(sample), labeller = as_labeller(mouse_labels)) +
small_axis(label = "spatial coord.", fontsize = font_size_small) +
labs(color = "Plaque density", subtitle = "Spatial structure") +
guides(color = guide_colorbar(barheight = unit(1, "mm"))) +
theme(legend.position = "top", strip.text = element_text(size = font_size_tiny, margin = margin(b = -1, unit = "mm")),
plot.subtitle = element_text(size = 7))
spatial_plaq_dens_pl
set.seed(1)
umap <- uwot::umap(t(fit$embedding))
as_tibble(colData(fit)) %>%
mutate(umap = umap) %>%
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = sample), size = 0.3, stroke = 0) +
guides(color = guide_legend(override.aes = list(size = 1)))
as_tibble(colData(fit)) %>%
mutate(subclass = fct_lump(subclass, n = 5)) %>%
mutate(umap = umap) %>%
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = subclass == "DG"), size = 0.3, stroke = 0) +
guides(color = guide_legend(override.aes = list(size = 1)))
as_tibble(colData(fit)) %>%
mutate(umap = umap) %>%
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = plaque_cluster), size = 0.3, stroke = 0) +
guides(color = guide_legend(override.aes = list(size = 1)))
umap_plt <- as_tibble(colData(fit)) %>%
mutate(umap = umap) %>%
ggplot(aes(x = umap[,2], y = umap[,1])) +
ggrastr::rasterize(geom_point(aes(color = plaque_density), size = 0.1, stroke = 0, show.legend = FALSE), dpi = 600) +
scale_color_gradient(low = "lightgrey", high = "darkorange", limits = c(0, 1), breaks = c(0, 0.5, 1)) +
small_axis(label = "UMAP", fontsize = font_size_small) +
labs(subtitle = "Latent structure") +
theme(plot.subtitle = element_text(size = 7))
umap_plt
genes_of_interest <- nei$name[1]
master_data <- as_tibble(colData(fit)) %>%
mutate(inside = row_number() %in% nei$neighborhood[[1]]) %>%
mutate(umap = umap) %>%
mutate(de = assay(fit, "DE")[genes_of_interest[1], ])
make_spatial_plot <- function(data, color_by = NULL){
data %>%
filter(sample %in% c("mouse_1", "mouse_4")) %>%
ggplot(aes(x = x, y = y)) +
ggrastr::rasterize(geom_point(aes(color = {{color_by}}), size = 0.08, stroke = 0), dpi = 600) +
facet_wrap(vars(sample), labeller = as_labeller(mouse_labels), nrow = 2) +
small_axis(label = "spatial coord.", fontsize = font_size_small) +
theme(strip.text = element_text(size = font_size_tiny, margin = margin(b = -1, unit = "mm")),
legend.position = "top",
legend.title = element_text(size = font_size),
legend.text = element_text(size = font_size_small),
plot.subtitle = element_text(size = 7))
}
make_spatial_plot(master_data, color_by = plaque_density) +
scale_color_gradient(low = "lightgrey", high = "darkorange", limits = c(0, 1), breaks = c(0, 0.5, 1)) +
guides(color = guide_colorbar(barheight = unit(1, "mm"), label.vjust = 3)) +
labs(color = "")
make_umap_plot <- function(data, color_by = NULL){
ggplot(data, aes(x = umap[,2], y = umap[,1])) +
ggrastr::rasterize(geom_point(aes(color = {{color_by}}), size = 0.08, stroke = 0), dpi = 600) +
small_axis(label = "UMAP", fontsize = font_size_small) +
theme(strip.text = element_text(size = font_size_tiny, margin = margin(b = -1, unit = "mm")),
plot.subtitle = element_text(size = 7))
}
table(fit$colData$class, fit$colData$subclass)
##
## Astro CA1-ProS CA2-IG-FC CA3 CR CT SUB DG Endo
## GABAergic 0 0 0 0 0 0 0 0
## Glutamatergic 0 9618 169 1290 53 0 13852 0
## Non-Neuronal 3160 0 0 0 0 0 0 180
##
## L2 IT ENTl L2/3 IT CTX L2/3 IT ENTl L2/3 IT PPP L2/3 IT RHP
## GABAergic 0 0 0 0 0
## Glutamatergic 0 0 0 1 0
## Non-Neuronal 0 0 0 0 0
##
## L4/5 IT CTX L5 PT CTX L6 CT CTX L6 IT CTX L6b CTX Lamp5
## GABAergic 0 0 0 0 0 3082
## Glutamatergic 0 0 0 0 0 0
## Non-Neuronal 0 0 0 0 0 0
##
## Micro-PVM NP SUB Oligo Pvalb SMC-Peri SUB-ProS Sncg Sst
## GABAergic 0 0 0 208 0 0 1088 817
## Glutamatergic 0 110 0 0 0 66 0 0
## Non-Neuronal 640 0 6278 0 88 0 0 0
##
## Sst Chodl VLMC Vip
## GABAergic 0 0 333
## Glutamatergic 0 0 0
## Non-Neuronal 0 22 0
subclass_name_table <- c("DG" = "Dentate gyrus", "CA1-ProS" = "CA1\n(prosubiculum)", "Oligo" = "Oligodendrocytes",
"Astro" = "Astrocytes", "CA3" = "CA3", "CA2-IG-FC" = "CA2", "CR" = "Cajal-Retzius",
"Endo" = "Endothelium", "NP SUB" = "Cortex L5/6\nnear-projecting", "SUB-ProS" = "Subiculum/prosubiculum",
"L2/3 IT PPP" = "L2/3 IT PPP"
)
full_cell_types_pl <- master_data %>%
mutate(subclass_label = case_when(
class == "GABAergic" ~ "GABAergic Neuron",
subclass %in% c("Micro-PVM", "VLMC", "SMC-Peri") ~ "Immune/vascular cells",
subclass %in% names(subclass_name_table) ~ subclass_name_table[as.character(subclass)],
TRUE ~ paste0("MISSING: ", subclass)
)) %>%
filter(subclass_label != "L2/3 IT PPP") %>% # It's only one cell
mutate(subclass_label = fct_infreq(subclass_label)) %>%
ggplot(aes(x = umap[,2], y = umap[,1])) +
ggrastr::rasterize(geom_point(aes(color = subclass_label), size = 0.08, stroke = 0, show.legend = FALSE), dpi = 300) +
geom_density2d(data = master_data %>% filter(inside), breaks = 0.03, contour_var = "ndensity",
color = "black", linetype = "dashed", linewidth = 0.6) +
small_axis(label = "UMAP", fontsize = font_size_small) +
guides(color = guide_legend(override.aes = list(size = 3))) +
facet_wrap(vars(subclass_label), ncol = 6)
full_cell_types_pl
convert_interval_to_number <- function(interval){
str_mat <- str_match(interval, ".(\\d+\\.?\\d*),(\\d+\\.?\\d*)")[,2:3]
num_mat <- array(as.numeric(str_mat), dim = dim(str_mat))
rowMeans(num_mat)
}
plaque_expr_relation_plt <- as_tibble(colData(fit)) %>%
mutate(expr = logcounts(fit)[1,]) %>%
mutate(inside = row_number() %in% nei$neighborhood[[1]]) %>%
mutate(subclass_label = case_when(
class == "GABAergic" ~ "GABAergic Neuron",
subclass %in% c("Micro-PVM", "VLMC", "SMC-Peri") ~ "Immune/vascular cells",
subclass %in% names(subclass_name_table) ~ subclass_name_table[as.character(subclass)],
TRUE ~ paste0("MISSING: ", subclass)
)) %>%
filter(subclass_label != "L2/3 IT PPP") %>% # It's only one cell
add_count(subclass_label, inside) %>%
mutate(subclass_label = fct_infreq(subclass_label)) %>%
summarize(expr = mean(expr), .by = c(plaque_cluster, subclass_label, inside, sample, n)) %>%
mutate(plaque_cluster_cont = convert_interval_to_number(plaque_cluster)) %>%
mutate(n_label_insert = ifelse(inside, "Cells inside:\\;\\;\\;", "Cells outside: ")) %>%
mutate(n_label = paste0(n_label_insert, prettyNum(n, big.mark = "\\\\,"))) %>%
ggplot(aes(x = plaque_cluster_cont, y = expr)) +
geom_text(data = . %>% filter(!inside) %>% distinct(subclass_label, n_label), aes(x = 0.02, y = Inf, label = n_label),
vjust = 1.1, hjust = 0, size = font_size_tiny / .pt) +
geom_text(data = . %>% filter(inside) %>% distinct(subclass_label, n_label), aes(x = 0.02, y = Inf, label = n_label),
vjust = 2.8, hjust = 0, size = font_size_tiny / .pt) +
geom_smooth(aes(color = inside), span = 1.5, se = FALSE, linewidth = 2, show.legend = FALSE) +
geom_point(aes(color = stage(inside, after_scale = colorspace::darken(color, 0.2))), size = 0.3) +
scale_color_manual(values = c("TRUE" = "darkblue", "FALSE" = "darkgrey")) +
scale_x_continuous(limits = c(0, 1), breaks = c(0, 0.5, 1), labels = c("0", "0.5", "1"),
expand = expansion(add = 0)) +
coord_cartesian(ylim = c(0, 0.5)) +
facet_wrap(vars(subclass_label), scales = "fixed", ncol = 6) +
guides(color = guide_legend(override.aes = list(size = 2))) +
theme(panel.spacing.x = unit(4, "mm"), legend.position = "bottom") +
labs(color = "Inside \\emph{Jun}'s\nneighborhood",
x = "Amyloid-$\\beta$ density",
y = "\\emph{Jun} expression (pseudobulked)")
plot_assemble(
add_text("(A) Alzheimer's disease mouse model cell type details", x = 0.5, y = 1, fontsize = font_size, vjust = 1, fontface = "bold"),
add_plot(full_cell_types_pl, x = 8, y = 3, width = 162, height = 100),
add_text("(B) Relation of Amyloid-$\\beta$ density and \\emph{Jun} expression per cell type", x = 0.5, y = 104, fontsize = font_size, vjust = 1, fontface = "bold"),
add_plot(plaque_expr_relation_plt, x = 0, y = 108, width = 170, height = 66),
width = 170, height = 176, units = "mm", show_grid_lines = FALSE,
latex_support = TRUE, filename = "../plots/suppl_alzheimer_cell_types.pdf"
)
## Using TikZ metrics dictionary at:
## alzheimer_plaques_analysis-tikzDictionary
## gg[gg1]
## gg[gg2]
## gg[gg3]
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : span too small. fewer data values than degrees of freedom.
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 0.0455
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1.1078
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 1.2272
## gg[gg4]
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
amb_sp <- make_spatial_plot(master_data, color_by = plaque_density) +
scale_color_gradient(low = "lightgrey", high = "darkorange", limits = c(0, 1),
breaks = c(0, 0.5, 1), labels = c("0", "0.5", "1")) +
guides(color = guide_colorbar(barheight = unit(1, "mm"), label.vjust = 3)) +
labs(color = "")
amb_umap <- make_umap_plot(master_data, color_by = plaque_density) +
scale_color_gradient(low = "lightgrey", high = "darkorange", limits = c(0, 1), breaks = c(0, 0.5, 1)) +
guides(color = "none")
ct_master_data <- master_data %>%
mutate(is_neuron = ifelse(class %in% c("Glutamatergic", "GABAergic"), "neuron", "other"))
ct_sp <- make_spatial_plot(ct_master_data, color_by = is_neuron) +
scale_color_manual(values = c("neuron" = "lightblue", "other" = "lightgrey"), labels = c("neuron"="Neuron", "other"="other cell type")) +
guides(color = guide_legend(override.aes = list(size = 3))) +
labs(color = "") +
theme(legend.text = element_text(size = font_size))
ct_umap <- make_umap_plot(ct_master_data, color_by = is_neuron) +
scale_color_manual(values = c("neuron" = "lightblue", "other" = "lightgrey")) +
guides(color = "none")
abs_max <- max(abs(quantile(assay(fit, "DE")["Jun", ], prob = c(0.05, 0.95))))
de_sp <- make_spatial_plot(master_data, color_by = de) +
guides(color = guide_colorbar(barheight = unit(1, "mm"), label.vjust = 3)) +
scale_color_de_gradient(abs_max = 0.2, breaks = c(-0.2, 0, 0.2), labels = c("-0.2", "0", "0.2")) +
labs(color = "")
de_umap <- make_umap_plot(master_data, color_by = de) +
scale_color_de_gradient(abs_max = abs_max) +
guides(color = "none")
nei_master_data <- master_data %>%
mutate(inside = ifelse(inside, "in", "out"))
nei_sp <- make_spatial_plot(nei_master_data, color_by = inside) +
scale_color_manual(values = c("in" = "darkblue", "out" = "lightgrey"), labels = c("in"="Inside \\emph{Jun} neighborhood", "out"="outside")) +
guides(color = guide_legend(override.aes = list(size = 3))) +
labs(color = "") +
theme(legend.text = element_text(size = font_size))
nei_umap <- make_umap_plot(nei_master_data, color_by = inside) +
scale_color_manual(values = c("in" = "darkblue", "out" = "lightgrey")) +
guides(color = "none")
genes_of_interest <- nei$name[1]
mask <- matrix(NA, nrow = length(genes_of_interest), ncol = ncol(fit),
dimnames = list(genes_of_interest, colnames(fit)))
mask2 <- matrix(NA, nrow = length(genes_of_interest), ncol = ncol(fit),
dimnames = list(genes_of_interest, colnames(fit)))
mask3 <- matrix(NA, nrow = length(genes_of_interest), ncol = ncol(fit),
dimnames = list(genes_of_interest, colnames(fit)))
for(id in genes_of_interest){
mask[id, filter(nei, name == id)$neighborhood[[1]]] <- 1
mask2[id, ! fit$colData$class %in% c("Glutamatergic", "GABAergic")] <- 1
mask2[id, filter(nei, name == id)$neighborhood[[1]]] <- NA
mask3[id, fit$colData$class %in% c("Glutamatergic", "GABAergic")] <- 1
mask3[id, filter(nei, name == id)$neighborhood[[1]]] <- NA
}
psce2 <- glmGamPoi::pseudobulk(SingleCellExperiment(list(inside = as.matrix(logcounts(fit)[genes_of_interest,,drop=FALSE] * mask),
outside = as.matrix(logcounts(fit)[genes_of_interest,,drop=FALSE] * mask2),
outside_neurons = as.matrix(logcounts(fit)[genes_of_interest,,drop=FALSE] * mask3))),
group_by = vars(sample, plaque_cluster), n = n(),
aggregation_functions = list(.default = \(...) matrixStats::rowMeans2(..., na.rm = TRUE)),
col_data = as.data.frame(colData(fit)))
## Aggregating assay 'inside' using 'custom function'.
## Aggregating assay 'outside' using 'custom function'.
## Aggregating assay 'outside_neurons' using 'custom function'.
comparison_data <- as_tibble(colData(psce2)) %>%
mutate(expr_inside = as_tibble(t(assay(psce2, "inside"))),
expr_outside = as_tibble(t(assay(psce2, "outside"))),
expr_outsideNeuron = as_tibble(t(assay(psce2, "outside_neurons")))) %>%
unpack(starts_with("expr"), names_sep = "-") %>%
pivot_longer(starts_with("expr"), names_sep = "[-_]", names_to = c(".value", "origin", "symbol"))
expr_comparison_pl <- comparison_data %>%
mutate(plaque_cluster_mean = convert_interval_to_number(as.character(plaque_cluster))) %>%
mutate(origin = factor(origin, levels = c("inside", "outsideNeuron", "outside"))) %>%
ggplot(aes(x = plaque_cluster_mean, y = expr)) +
geom_point(aes(color = origin), size = 0.3) +
geom_smooth(aes(color = origin), span = 1.5, se = FALSE, linewidth = 2) +
scale_color_manual(values = c("inside" = "darkblue", "outsideNeuron" = "lightblue", "outside" = "lightgrey"),
labels = c("inside" = "Cells in neighborhood", "outsideNeuron" = "Other neurons", "outside" = "All other cells")) +
scale_y_continuous(limits = c(0, 0.3), expand = expansion(add = 0)) +
scale_x_continuous(breaks = seq(0, 1, by = 0.1), limits = c(0, 1)) +
theme(legend.position = "right") +
labs(color = "", y = "Expression", x = "Amyloid-$\\beta$ plaque density (binned)",
subtitle = "\\emph{Jun} expr. vs. Amyloid-$\\beta$ plaque density") +
theme(plot.subtitle = element_text(size = font_size))
expr_comparison_pl
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
writexl::write_xlsx(list("Panel A-D" = unpack_2d(master_data),
"Panel E" = comparison_data),
path = "../source_data/alzheimer_figure.xlsx")
plot_assemble(
add_text("(A) ", x = 0.5, y = 1, fontsize = font_size, vjust = 1, fontface = "bold"),
add_text("Amyloid-$\\beta$ density", x = 6, y = 2, fontsize = font_size, vjust = 1, fontface = "plain"),
add_plot(my_get_legend(amb_sp), x = 37, y = 3, height = 5, width = 1),
add_plot(amb_sp + guides(color = "none"), x = 2, y = 5, width = 23, height = 45),
add_plot(amb_umap, x = 22, y = 5, width = 33, height = 45),
add_text("(B) ", x = 56, y = 1, fontsize = font_size, vjust = 1, fontface = "bold"),
add_plot(my_get_legend(ct_sp), x = 84, y = 0.5, height = 5, width = 1),
add_plot(ct_sp + guides(color = "none"), x = 58, y = 5, width = 23, height = 45),
add_plot(ct_umap, x = 78, y = 5, width = 33, height = 45),
add_text("(C) ", x = 112, y = 1, fontsize = font_size, vjust = 1, fontface = "bold"),
add_text("\\emph{Jun} differential expression", x = 118, y = 2, fontsize = font_size, vjust = 1, fontface = "plain"),
add_plot(my_get_legend(de_sp), x = 159, y = 3, height = 5, width = 1),
add_plot(de_sp + guides(color = "none"), x = 114, y = 5, width = 23, height = 45),
add_plot(de_umap, x = 134, y = 5, width = 33, height = 45),
add_text("(D) ", x = 0.5, y = 50, fontsize = font_size, vjust = 1, fontface = "bold"),
add_plot(nei_sp, x = 2, y = 50, width = 23, height = 50),
add_plot(nei_umap, x = 22, y = 55, width = 33, height = 45),
add_text("(E) ", x = 65, y = 50, fontsize = font_size, vjust = 1, fontface = "bold"),
add_plot(expr_comparison_pl, x = 66, y = 50, width = 100, height = 50),
width = 170, height = 100, units = "mm", show_grid_lines = FALSE,
latex_support = TRUE, filename = "../plots/alzheimer_figure.pdf"
)
## gg[gg1]
## gg[gg2]
## gg[gg3]
## gg[gg4]
## gg[gg5]
## gg[gg6]
## gg[gg7]
## gg[gg8]
## gg[gg9]
## gg[gg10]
## gg[gg11]
## gg[gg12]
## gg[gg13]
## gg[gg14]
## gg[gg15]
## gg[gg16]
## gg[gg17]
## gg[gg18]
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## gg[gg19]
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
master_data %>%
mutate(label = case_when(
inside ~ "inside",
cell_type == "NEURON" & ! inside ~ "other neurons",
TRUE ~ "other",
)) %>%
sample_frac(n = 1) %>%
ggplot(aes(x = x, y = y)) +
geom_point(aes(color = label), size = 0.1) +
facet_wrap(vars(sample))
